\(\mathcal{YiHsin}\;\mathcal{Lu}\)

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Outline

  • Introduction
  • Data set
  • Text Mining
  • Multi-value Classification
  • Contents
  • Remark
  • Reference

1. Introduction

2. Data set

6 variables, 236 musicians

  • abstract
  • yearsActive
  • genre
  • recordLabel
  • instrument
  • occupation

2.1 Abstract

2.2 Data Matrix

3. Text Mining

3.1 Abstract

one-gram

one-gram token

two-grams

two-grams token

3.2 Token Matrix

  • tokens: 94 from 23070

4. Multi-value Classification

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4.1 Compress \(\mathcal{Y}\) information

Y-info.

Musicians

Instruments

4.2 PCA on Instrument Affinity Matrix

4.2.1 PC1 vs PC2

4.2.2 PC2 vs PC3

4.2.3 PC1 vs PC3

4.3 Correlation

Instrument

Musicians

5. Contents

Title: Visualizing Jazz Musicians

  • Abstract
  • Introduction
    • motivation
    • linked jazz
    • jazz musicians
  • Data matrix
    • collection
    • text mining
  • Multi-value Classification
    • “Y” information
    • method
      • PCA
      • correlation
  • Visualization
    • method
      • t-SNE
      • St-SNE
      • LDA
    • visualizing
      • (fig) color by instrument
      • (fig) color by genre
      • (fig) color by yearActive
  • Conclusion and future work
  • Discussion
  • Reference
  • Appendix
    • python code
    • R code

6. Remark

  • The way out of multi-value classification
  • Group by PCA on instrument